Overview

Dataset statistics

Number of variables15
Number of observations5705
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory668.7 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
recency_days is highly correlated with qnt_purchasesHigh correlation
qnt_purchases is highly correlated with gross_revenue and 5 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qtd_returned is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with qnt_purchasesHigh correlation
qnt_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returned and 1 other fieldsHigh correlation
qtd_returned is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
freq_purchase is highly correlated with qnt_purchasesHigh correlation
qtd_returned is highly correlated with freq_returns and 2 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_productsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
df_index is highly correlated with customer_id and 1 other fieldsHigh correlation
customer_id is highly correlated with df_index and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 2 other fieldsHigh correlation
recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly correlated with qnt_items and 2 other fieldsHigh correlation
qtd_returned is highly correlated with qnt_items and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with qnt_items and 2 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly skewed (γ1 = 22.60448601) Skewed
qnt_items is highly skewed (γ1 = 24.08929936) Skewed
avg_ticket is highly skewed (γ1 = 71.36561628) Skewed
qtd_returned is highly skewed (γ1 = 71.12000871) Skewed
avg_basket_size is highly skewed (γ1 = 58.37737567) Skewed
item_rp_ratio is highly skewed (γ1 = 75.47735114) Skewed
net_margin is highly skewed (γ1 = -74.26063732) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
qtd_returned has 4200 (73.6%) zeros Zeros
freq_returns has 4200 (73.6%) zeros Zeros
item_rp_ratio has 4200 (73.6%) zeros Zeros

Reproduction

Analysis started2021-10-22 18:36:59.703429
Analysis finished2021-10-22 18:37:21.785251
Duration22.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5705
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2900.82752
Minimum0
Maximum5795
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:21.851650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile290.2
Q11457
median2903
Q34348
95-th percentile5503.8
Maximum5795
Range5795
Interquartile range (IQR)2891

Descriptive statistics

Standard deviation1671.81192
Coefficient of variation (CV)0.5763224145
Kurtosis-1.196226531
Mean2900.82752
Median Absolute Deviation (MAD)1446
Skewness-0.003583510807
Sum16549221
Variance2794955.097
MonotonicityStrictly increasing
2021-10-22T15:37:21.945885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
38521
 
< 0.1%
38721
 
< 0.1%
38711
 
< 0.1%
38701
 
< 0.1%
38691
 
< 0.1%
38681
 
< 0.1%
38671
 
< 0.1%
38661
 
< 0.1%
38651
 
< 0.1%
Other values (5695)5695
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57951
< 0.1%
57941
< 0.1%
57931
< 0.1%
57921
< 0.1%
57911
< 0.1%
57901
< 0.1%
57891
< 0.1%
57881
< 0.1%
57871
< 0.1%
57861
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5705
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16602.92235
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.044548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12697.6
Q114288
median16229
Q318213
95-th percentile21746.2
Maximum22709
Range10363
Interquartile range (IQR)3925

Descriptive statistics

Standard deviation2811.170356
Coefficient of variation (CV)0.1693178043
Kurtosis-0.8232857661
Mean16602.92235
Median Absolute Deviation (MAD)1964
Skewness0.4409745831
Sum94719672
Variance7902678.772
MonotonicityNot monotonic
2021-10-22T15:37:22.143203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
210711
 
< 0.1%
171231
 
< 0.1%
178911
 
< 0.1%
164981
 
< 0.1%
137451
 
< 0.1%
155841
 
< 0.1%
210891
 
< 0.1%
210881
 
< 0.1%
210871
 
< 0.1%
Other values (5695)5695
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5459
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1772.229166
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.237602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.32
Q1236.3
median612.78
Q31568.23
95-th percentile5305.816
Maximum279138.02
Range279137.6
Interquartile range (IQR)1331.93

Descriptive statistics

Standard deviation7575.73186
Coefficient of variation (CV)4.274690885
Kurtosis676.7694665
Mean1772.229166
Median Absolute Deviation (MAD)478.32
Skewness22.60448601
Sum10110567.39
Variance57391713.21
MonotonicityNot monotonic
2021-10-22T15:37:22.325361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
2.958
 
0.1%
4.958
 
0.1%
1.258
 
0.1%
3.757
 
0.1%
1.657
 
0.1%
12.757
 
0.1%
4.256
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
Other values (5449)5633
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9258545
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.418554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.5740338
Coefficient of variation (CV)0.9542289363
Kurtosis-0.6403097319
Mean116.9258545
Median Absolute Deviation (MAD)61
Skewness0.8143906836
Sum667062
Variance12448.76501
MonotonicityNot monotonic
2021-10-22T15:37:22.512279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
292
 
1.6%
1086
 
1.5%
882
 
1.4%
979
 
1.4%
1779
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4829
84.6%
ValueCountFrequency (%)
037
 
0.6%
1110
1.9%
292
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37223
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.468010517
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.610640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.807826068
Coefficient of variation (CV)1.963035012
Kurtosis302.5604586
Mean3.468010517
Median Absolute Deviation (MAD)0
Skewness13.20280307
Sum19785
Variance46.34649578
MonotonicityNot monotonic
2021-10-22T15:37:22.700438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12876
50.4%
2829
 
14.5%
3504
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12876
50.4%
2829
 
14.5%
3504
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%

var_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct529
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.53531989
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.793031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median41
Q3106
95-th percentile331.8
Maximum7838
Range7837
Interquartile range (IQR)92

Descriptive statistics

Standard deviation210.4062511
Coefficient of variation (CV)2.273793957
Kurtosis511.099444
Mean92.53531989
Median Absolute Deviation (MAD)33
Skewness17.76686848
Sum527914
Variance44270.79052
MonotonicityNot monotonic
2021-10-22T15:37:22.891613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1256
 
4.5%
2149
 
2.6%
3108
 
1.9%
10101
 
1.8%
699
 
1.7%
992
 
1.6%
591
 
1.6%
487
 
1.5%
1184
 
1.5%
783
 
1.5%
Other values (519)4555
79.8%
ValueCountFrequency (%)
1256
4.5%
2149
2.6%
3108
1.9%
487
 
1.5%
591
 
1.6%
699
 
1.7%
783
 
1.5%
881
 
1.4%
992
 
1.6%
10101
 
1.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1840
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963.3996494
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:22.992406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3804
95-th percentile2926.4
Maximum196844
Range196843
Interquartile range (IQR)698

Descriptive statistics

Standard deviation4296.512152
Coefficient of variation (CV)4.459740207
Kurtosis864.8815057
Mean963.3996494
Median Absolute Deviation (MAD)253
Skewness24.08929936
Sum5496195
Variance18460016.68
MonotonicityNot monotonic
2021-10-22T15:37:23.089145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1830)5266
92.3%
ValueCountFrequency (%)
1114
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5511
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.69523826
Minimum0.42
Maximum77183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:23.189569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.462222222
Q17.95
median15.85882353
Q321.97516949
95-th percentile76.24171429
Maximum77183.6
Range77183.18
Interquartile range (IQR)14.02516949

Descriptive statistics

Standard deviation1042.883788
Coefficient of variation (CV)23.33321913
Kurtosis5254.947144
Mean44.69523826
Median Absolute Deviation (MAD)7.494723111
Skewness71.36561628
Sum254986.3343
Variance1087606.596
MonotonicityNot monotonic
2021-10-22T15:37:23.286079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7511
 
0.2%
4.9510
 
0.2%
1.259
 
0.2%
2.959
 
0.2%
7.958
 
0.1%
1.657
 
0.1%
12.757
 
0.1%
8.257
 
0.1%
3.356
 
0.1%
4.156
 
0.1%
Other values (5501)5625
98.6%
ValueCountFrequency (%)
0.423
0.1%
0.5351
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
< 0.1%
0.853
0.1%
1.0022222221
 
< 0.1%
1.021
 
< 0.1%
1.038751
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
3202.921
< 0.1%
30961
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%
952.98751
< 0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5475900988
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:23.383954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01104972376
Q10.025
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.975

Descriptive statistics

Standard deviation0.5504405784
Coefficient of variation (CV)1.005205499
Kurtosis138.6978064
Mean0.5475900988
Median Absolute Deviation (MAD)0
Skewness4.846833994
Sum3124.001514
Variance0.3029848303
MonotonicityNot monotonic
2021-10-22T15:37:23.481958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12884
50.6%
248
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0833333333315
 
0.3%
0.0909090909115
 
0.3%
0.0344827586215
 
0.3%
0.0294117647114
 
0.2%
0.0769230769213
 
0.2%
Other values (1216)2650
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
0.8%
1.1428571431
 
< 0.1%
12884
50.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.2138475
Minimum0
Maximum74215
Zeros4200
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:23.578926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38
Maximum74215
Range74215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1003.441796
Coefficient of variation (CV)32.14732808
Kurtosis5241.542158
Mean31.2138475
Median Absolute Deviation (MAD)0
Skewness71.12000871
Sum178075
Variance1006895.439
MonotonicityNot monotonic
2021-10-22T15:37:23.671079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.6%
1169
 
3.0%
2151
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (204)713
 
12.5%
ValueCountFrequency (%)
04200
73.6%
1169
 
3.0%
2151
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
742151
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct428
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1621964162
Minimum0
Maximum4
Zeros4200
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:23.769156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01016949153
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.01016949153

Descriptive statistics

Standard deviation0.3734871455
Coefficient of variation (CV)2.302684327
Kurtosis4.821364232
Mean0.1621964162
Median Absolute Deviation (MAD)0
Skewness2.194591618
Sum925.3305543
Variance0.1394926479
MonotonicityNot monotonic
2021-10-22T15:37:23.864254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.6%
1857
 
15.0%
214
 
0.2%
0.58
 
0.1%
0.28571428578
 
0.1%
0.025641025647
 
0.1%
0.256
 
0.1%
0.0094786729865
 
0.1%
0.22222222225
 
0.1%
0.012987012995
 
0.1%
Other values (418)590
 
10.3%
ValueCountFrequency (%)
04200
73.6%
0.0055710306411
 
< 0.1%
0.0056818181822
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
214
 
0.2%
1857
15.0%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.58
 
0.1%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2370
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.1061465
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:23.961307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.625
95-th percentile733.1
Maximum74215
Range74214
Interquartile range (IQR)215.625

Descriptive statistics

Standard deviation1073.214896
Coefficient of variation (CV)4.110262859
Kurtosis3964.741092
Mean261.1061465
Median Absolute Deviation (MAD)96.5
Skewness58.37737567
Sum1489610.566
Variance1151790.214
MonotonicityNot monotonic
2021-10-22T15:37:24.053719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
10022
 
0.4%
7222
 
0.4%
7321
 
0.4%
Other values (2360)5263
92.3%
ValueCountFrequency (%)
1115
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1171
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.24696204
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:24.150843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.25
median15.07142857
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation76.82077129
Coefficient of variation (CV)2.062470792
Kurtosis32.94424993
Mean37.24696204
Median Absolute Deviation (MAD)9.928571429
Skewness5.077303066
Sum212493.9185
Variance5901.430901
MonotonicityNot monotonic
2021-10-22T15:37:24.243344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1278
 
4.9%
2161
 
2.8%
3115
 
2.0%
10105
 
1.8%
9105
 
1.8%
8103
 
1.8%
5101
 
1.8%
7101
 
1.8%
6101
 
1.8%
1397
 
1.7%
Other values (1161)4438
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

item_rp_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1382
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06254345309
Minimum0
Maximum282
Zeros4200
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.7 KiB
2021-10-22T15:37:24.335558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.001086956522
95-th percentile0.04131473197
Maximum282
Range282
Interquartile range (IQR)0.001086956522

Descriptive statistics

Standard deviation3.734264988
Coefficient of variation (CV)59.70672874
Kurtosis5699.532667
Mean0.06254345309
Median Absolute Deviation (MAD)0
Skewness75.47735114
Sum356.8103999
Variance13.944735
MonotonicityNot monotonic
2021-10-22T15:37:24.423409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04200
73.6%
112
 
0.2%
0.010752688174
 
0.1%
0.0096618357494
 
0.1%
0.0074626865673
 
0.1%
0.038461538463
 
0.1%
0.53
 
0.1%
0.02439024393
 
0.1%
0.023809523813
 
0.1%
0.014925373133
 
0.1%
Other values (1372)1467
 
25.7%
ValueCountFrequency (%)
04200
73.6%
0.00011696362431
 
< 0.1%
0.00018399264031
 
< 0.1%
0.00028169014081
 
< 0.1%
0.00031407035181
 
< 0.1%
0.00036192544341
 
< 0.1%
0.00036324010171
 
< 0.1%
0.00036376864311
 
< 0.1%
0.00036710719531
 
< 0.1%
0.0003930817611
 
< 0.1%
ValueCountFrequency (%)
2821
 
< 0.1%
3.2758620691
 
< 0.1%
1.5519125681
 
< 0.1%
112
0.2%
0.98630136991
 
< 0.1%
0.83333333331
 
< 0.1%
0.63333333331
 
< 0.1%
0.61151079141
 
< 0.1%
0.60088365241
 
< 0.1%
0.59645669291
 
< 0.1%

net_margin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1498
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9746389265
Minimum-53.124
Maximum1
Zeros12
Zeros (%)0.2%
Negative3
Negative (%)0.1%
Memory size44.7 KiB
2021-10-22T15:37:24.516409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-53.124
5-th percentile0.9299445896
Q10.9978968552
median1
Q31
95-th percentile1
Maximum1
Range54.124
Interquartile range (IQR)0.00210314482

Descriptive statistics

Standard deviation0.720476139
Coefficient of variation (CV)0.7392236442
Kurtosis5575.872973
Mean0.9746389265
Median Absolute Deviation (MAD)0
Skewness-74.26063732
Sum5560.315076
Variance0.5190858669
MonotonicityNot monotonic
2021-10-22T15:37:24.612766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13722
65.2%
1224
 
3.9%
1161
 
2.8%
193
 
1.6%
012
 
0.2%
0.98097273151
 
< 0.1%
0.99819346331
 
< 0.1%
0.99476653251
 
< 0.1%
0.96312878591
 
< 0.1%
0.97968570621
 
< 0.1%
Other values (1488)1488
 
26.1%
ValueCountFrequency (%)
-53.1241
 
< 0.1%
-1.3696030981
 
< 0.1%
-0.12187003751
 
< 0.1%
012
0.2%
0.049466537341
 
< 0.1%
0.13368983961
 
< 0.1%
0.14017054081
 
< 0.1%
0.25023409121
 
< 0.1%
0.28295102291
 
< 0.1%
0.3241179911
 
< 0.1%
ValueCountFrequency (%)
1224
 
3.9%
13722
65.2%
1161
 
2.8%
193
 
1.6%
0.99991807691
 
< 0.1%
0.99984316131
 
< 0.1%
0.99972431321
 
< 0.1%
0.99969169041
 
< 0.1%
0.99967287611
 
< 0.1%
0.99961497151
 
< 0.1%

Interactions

2021-10-22T15:37:19.946002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:00.867885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.225305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.597818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.877554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.308957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.569056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.043103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.392244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.883242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.219158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.535956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.045516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.374022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.669644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.027094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:00.978228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.308969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.683033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.964938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.392615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.656788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.133594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.478914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.970053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.306234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.622773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.131952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.457780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.754371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.107257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.070394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.389190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.766002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.142941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.474809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.743604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.222793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.564928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.055554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.391805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.710066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.217418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.539335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.837695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.186038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.153723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.471926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.848007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.225213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.556349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.830686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.310010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.651087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.141665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.475001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.794240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.301087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.621387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.920951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.269800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.240885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.567669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.935777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.314706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.641267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.035390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.402045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.744844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.231611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.562227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.882574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.391407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.707899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.006071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.346378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.318966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.646629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.015618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.397485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.717078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.120102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.486284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.829104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.317692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.643739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.974606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.475443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.786831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.082453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.432700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.407303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.815483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.104247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.490819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.803516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.214695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.580935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.923268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:15.066446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.568596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.880715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.170581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.520684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.496622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.904994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.191712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.587007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.891838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.310673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:11.015317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.509290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.828976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.157843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.662511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.972098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.256727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.607105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.586751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.995164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.279296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.681640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.982538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.404907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.770637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.108054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.603649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.920656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.248415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.757665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.060074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:01.672848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.083087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:05.775137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:08.497044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.862656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.201253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.693411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.010976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.337408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.848261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.148498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.433491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:20.778126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.759082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.170200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.456808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.868577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.158570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.591653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:09.955849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.294130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:15.428559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:16.938110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.235484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.521852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T15:37:01.847498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.260006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.546273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:05.962140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.248381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.691098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.048921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.528623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.875147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.193121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.522410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.028939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.327242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.609680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:21.163858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:01.934170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.351913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.633426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.053658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.332832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.783184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.138722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.620151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:12.964945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.282579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.790055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.117546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.415752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.698098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:21.244781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.015227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.433224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.714561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.138434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.411361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.869736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.222881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.707140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.049964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.368336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.873286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.202561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.499738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.779559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:21.328684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:02.102135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:03.516154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:04.796250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:06.222838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:07.489550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:08.955739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:10.307358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:11.794601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:13.134871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:14.452368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:15.960316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:17.288087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:18.581914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T15:37:19.863547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-22T15:37:24.704551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T15:37:24.845035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T15:37:24.985342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T15:37:25.126074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T15:37:21.487890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T15:37:21.675535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
00178505391.21372.034.0297.01733.018.15222217.00000040.01.00000050.9705880.6176470.0230810.980973
11130473232.5956.09.0171.01390.018.9040350.02830235.00.023973154.44444411.6666670.0251800.955611
22125836705.382.015.0232.05028.028.9025000.04032350.00.105263335.2000007.6000000.0099440.988660
3313748948.2595.05.028.0439.033.8660710.0179210.00.00000087.8000004.8000000.0000001.000000
4415100876.00333.03.03.080.0292.0000000.07317122.00.07894726.6666670.3333330.2750000.725000
55152914623.3025.014.0102.02102.045.3264710.04011529.00.032468150.1428574.3571430.0137960.984472
66146885630.877.021.0327.03621.017.2197860.057221399.00.019608172.4285717.0476190.1101910.907032
77178095411.9116.012.061.02057.088.7198360.03352041.00.013072171.4166673.8333330.0199320.987609
881531160767.900.091.02379.038194.025.5434640.243316474.00.072193419.7142866.2307690.0124100.977808
99160982005.6387.07.067.0613.029.9347760.0243900.00.00000087.5714294.8571430.0000001.000000

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
56955786227004839.421.01.062.01074.078.0551611.00.00.01074.055.00.01.0
5696578713298360.001.01.02.096.0180.0000001.00.00.096.02.00.01.0
5697578814569227.391.01.012.079.018.9491671.00.00.079.010.00.01.0
569857892270417.901.01.07.014.02.5571431.00.00.014.07.00.01.0
56995790227053.351.01.02.02.01.6750001.00.00.02.02.00.01.0
57005791227065699.001.01.0634.01747.08.9889591.00.00.01747.0634.00.01.0
57015792227076756.060.01.0730.02010.09.2548771.00.00.02010.0730.00.01.0
57025793227083217.200.01.059.0654.054.5288141.00.00.0654.056.00.01.0
57035794227093950.720.01.0217.0731.018.2060831.00.00.0731.0217.00.01.0
5704579512713794.550.01.037.0505.021.4743241.00.00.0505.037.00.01.0